Stuck on starting up page

[GitHubEmploy]

Hello, when I tried running this and using it, it ever worked. It was just stuck on this page.
C:\Users\Mohit\PycharmProjects\SuperAIPR\venv\Scripts\python.exe C:/Users/Mohit/PycharmProjects/SuperAIPR/armchair-expert/armchair_expert.py
INFO:ArmchairExpert:Status: STARTING_UP
INFO:ArmchairExpert:Loaded Discord Connector.
INFO:ArmchairExpert:Loading spaCy model
2020-11-10 13:41:58.346187: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
INFO:ArmchairExpert:Training begin
INFO:ArmchairExpert:Training_Preprocessing_Markov(Import)
INFO:ArmchairExpert:Training_Preprocessing_Markov(Discord)
INFO:ArmchairExpert:Training(Markov)
INFO:ArmchairExpert:Training_Preprocessing_Structure(Import)
INFO:ArmchairExpert:Training_Preprocessing_Structure(Discord)
INFO:ArmchairExpert:Training(Structure)
2020-11-10 13:41:58.930290: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
INFO:ArmchairExpert:Training end
INFO:ArmchairExpert:Status: RUNNING
2020-11-10 13:42:08.089287: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2020-11-10 13:42:08.173283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 680 computeCapability: 3.0
coreClock: 1.0585GHz coreCount: 8 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 179.05GiB/s
2020-11-10 13:42:08.174598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:20:00.0 name: Quadro P400 computeCapability: 6.1
coreClock: 1.2525GHz coreCount: 2 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 29.88GiB/s
2020-11-10 13:42:08.174914: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-11-10 13:42:08.234582: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-11-10 13:42:08.263904: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-11-10 13:42:08.272198: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-11-10 13:42:08.350489: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-11-10 13:42:08.376081: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-11-10 13:42:08.379728: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-11-10 13:42:08.380804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1812] Ignoring visible gpu device (device: 0, name: GeForce GTX 680, pci bus id: 0000:01:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
2020-11-10 13:42:08.381653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1843] Ignoring visible gpu device (device: 1, name: Quadro P400, pci bus id: 0000:20:00.0, compute capability: 6.1) with core count: 2. The minimum required count is 8. You can adjust this requirement with the env var TF_MIN_GPU_MULTIPROCESSOR_COUNT.
2020-11-10 13:42:08.432344: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1715f876120 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-11-10 13:42:08.432594: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-11-10 13:42:08.434576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-10 13:42:08.434815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]
Model: "sequential"

---

Layer (type) Output Shape Param #

embedding (Embedding) (None, 16, 120) 14400

---

lstm (LSTM) (None, 128) 127488

---

dense (Dense) (None, 120) 15480

Total params: 157,368
Trainable params: 157,368
Non-trainable params: 0

---

2020-11-10 13:42:09.270123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 680 computeCapability: 3.0
coreClock: 1.0585GHz coreCount: 8 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 179.05GiB/s
2020-11-10 13:42:09.270433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:20:00.0 name: Quadro P400 computeCapability: 6.1
coreClock: 1.2525GHz coreCount: 2 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 29.88GiB/s
2020-11-10 13:42:09.272560: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-11-10 13:42:09.273504: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-11-10 13:42:09.275615: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-11-10 13:42:09.275742: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-11-10 13:42:09.275866: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-11-10 13:42:09.275991: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-11-10 13:42:09.276120: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-11-10 13:42:09.277873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1812] Ignoring visible gpu device (device: 0, name: GeForce GTX 680, pci bus id: 0000:01:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
2020-11-10 13:42:09.278200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1843] Ignoring visible gpu device (device: 1, name: Quadro P400, pci bus id: 0000:20:00.0, compute capability: 6.1) with core count: 2. The minimum required count is 8. You can adjust this requirement with the env var TF_MIN_GPU_MULTIPROCESSOR_COUNT.
2020-11-10 13:42:09.279155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-10 13:42:09.279284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0 1
2020-11-10 13:42:09.279366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N N
2020-11-10 13:42:09.279511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1: N N
2020-11-10 13:42:09.281033: I tensorflow/compiler/xla/service/platform_util.cc:139] StreamExecutor cuda device (0) is of insufficient compute capability: 3.5 required, device is 3.0
2020-11-10 13:42:09.282005: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x17167ef3790 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-11-10 13:42:09.282233: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Quadro P400, Compute Capability 6.1
Its been like this for 20 minutes and I don't know what to do